1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
28/10/2024 Diosi 15000 Andrés NA
29/10/2024 Electricidad 50227 Andrés NA
31/10/2024 Electrodomésticos/mantención casa 13264 Andrés lona sombreadora
31/10/2024 Agua 12000 Andrés NA
4/11/2024 Comida 64915 Tami Supermercado
7/11/2024 Comida 34229 Andrés piwen
8/11/2024 Assistcard viaje 44836 Tami Assistcard viaje
9/11/2024 Comida 35916 Tami Supermercado
15/11/2024 Comida 12576 Tami Supermercado
17/11/2024 Comida 91203 Tami Supermercado
18/11/2024 Diosi 49500 Tami Veterinaria
23/11/2024 Comida 50929 Tami Supermercado
26/11/2024 Enceres 12990 Tami Confort 48 rollos
27/11/2024 Transporte 21780 Tami Transvip aeropuerto
27/11/2024 Electricidad 45000 Andrés Eléctrico
30/11/2024 Comida 62899 Tami Supermercado
11/12/2024 Comida 17738 Andrés compra lider despues viaje
12/12/2024 Comida 55616 Tami Supermercado
14/12/2024 Diosi 16276 Andrés antiparasitario
15/12/2024 Comida 23773 Tami Supermercado
18/12/2024 Comida 41554 Tami Supermercado
18/12/2024 VTR 22000 Andrés NA
20/12/2024 Plata basureros 10000 Tami NA
3/12/2024 Agua 15828 Andrés PAC AGUAS ANDIN 000000005687837
22/12/2024 Comida 46608 Tami Supermercado
10/12/2024 Otros 47484 Andrés viaje brasil
29/12/2024 Electricidad 27417 Andrés NA
29/12/2024 Comida 83284 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 9.7849e+08   2    4.8787 0.0078 ** 
## lag_depvar    2.6090e+11   1 2601.6691 <2e-16 ***
## Residuals     7.9724e+10 795                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -2001.493 16459.17 0.1576445
## 2-0 31580.425 23261.351 39899.50 0.0000000
## 2-1 24351.587 19521.410 29181.76 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
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## 29   28706.00             0   28640.00
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## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   643 53814.69 22643.903
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2026.81908   4043.77129   -541.41124   2435.17663  -2976.23516    516.34589 
##            8            9           10           11           12           13 
##  -5659.37729  -1183.71322  -3961.61328   -409.16038  -4932.15261  -1596.56218 
##           14           15           16           17           18           19 
##   -886.97098    389.21479  -3233.83166   -366.12966  -2119.96989   6615.29673 
##           20           21           22           23           24           25 
##  -1529.60633  -1207.24952   1477.48397  -1187.80132    234.53368   1693.84823 
##           26           27           28           29           30           31 
##  -7106.15548    953.36122   8195.53770    408.84587    -23.35066  -2409.33804 
##           32           33           34           35           36           37 
##   1571.32912   4565.42789   1113.71268   2377.74698  -1883.90636   4596.08354 
##           38           39           40           41           42           43 
##   4296.77189  -2287.60263  -2988.64971  -1112.28110 -10741.79711   7304.13797 
##           44           45           46           47           48           49 
##   2559.81326   1365.41129   8101.92907    671.85214   6515.25961   6693.36062 
##           50           51           52           53           54           55 
##  -5910.20925  -4811.52347  -5067.04549  -7927.85189   6141.58763  -4075.03282 
##           56           57           58           59           60           61 
##  -4887.25318   3868.82892    893.68758    -28.32985    145.50618  -4993.83641 
##           62           63           64           65           66           67 
##  18136.03934   3623.10225  -3666.61512   5912.59821   7324.55534  14611.55952 
##           68           69           70           71           72           73 
##   1648.77142 -13253.82403  -1323.24421   4630.49716  -4918.17525  -4413.14938 
##           74           75           76           77           78           79 
## -10498.62533   2481.00328  -5390.74418   1079.52522  -6853.91044    567.69778 
##           80           81           82           83           84           85 
##  -2337.10872  -2675.11774  -3911.51124   -513.94531   2336.06730   3778.08602 
##           86           87           88           89           90           91 
##    484.85248   -478.38450    202.60773   4306.80843  -1165.06608   1150.67328 
##           92           93           94           95           96           97 
##  -2066.35237  -1042.99932    180.17856    276.74243  -7482.77710   2403.77750 
##           98           99          100          101          102          103 
##  -8595.44759  -2921.70472  -4018.45547  -1712.20218  -1237.11700   3204.17195 
##          104          105          106          107          108          109 
##  -2326.36441   2611.20685  -1147.19065    981.96407   2595.70972  -3150.68658 
##          110          111          112          113          114          115 
##  -4715.19280   -836.42401   1916.90090  11702.45142  -1252.02284   2662.09378 
##          116          117          118          119          120          121 
##   4253.30700   3488.12284  -1117.75441  -4730.23063  -3729.25992   2320.79068 
##          122          123          124          125          126          127 
##  -1735.10825   1340.87408   8856.72024    832.81673    116.76741  -2533.36877 
##          128          129          130          131          132          133 
##   2648.23343   7042.69633    993.55028  -8517.36463   1745.98373   4130.14324 
##          134          135          136          137          138          139 
##  -3174.70771  -1424.48227   -856.03337  -3880.53401   1188.30654   -492.59842 
##          140          141          142          143          144          145 
##  -2910.34808   1725.30136  -1877.36509  -7823.29376   2056.21742  -3468.08391 
##          146          147          148          149          150          151 
##   2117.46820   -247.31649   1032.21170   -352.85523   1358.28447   1189.89409 
##          152          153          154          155          156          157 
##   3357.61383  -4865.88444  -1170.93741  -3231.02920   5965.62534   9745.61040 
##          158          159          160          161          162          163 
##  -3652.14446  -4995.55280   3391.50032    -26.13523   2472.83663  -6140.01334 
##          164          165          166          167          168          169 
##  -6969.10070   3943.28996  17169.04610   3367.37771   -664.49007  -2709.21025 
##          170          171          172          173          174          175 
##  -1361.14984   3336.43818   -488.54670  -8334.17121   2620.87311   4076.15413 
##          176          177          178          179          180          181 
##    367.66665   8491.97681  -9523.02996  -3726.10658 -10992.29073 -11468.19494 
##          182          183          184          185          186          187 
##   1024.94106   9075.56231  -1670.29980   5689.03039   6301.10433  12888.51085 
##          188          189          190          191          192          193 
##   8131.95488  -4379.04818   2159.96987  10059.73103  -1973.63251  -2766.80332 
##          194          195          196          197          198          199 
## -10593.76556  -6650.52152    961.39202  -5508.53388 -10059.23279   5141.77852 
##          200          201          202          203          204          205 
##  -3324.61603  -1962.99233  -1052.68311   6245.32857   9613.80112    283.28160 
##          206          207          208          209          210          211 
##   2628.82558   2796.09177   5476.93303  12514.94829  -6033.00110 -11620.69514 
##          212          213          214          215          216          217 
##  -5958.19390 -10863.22852  -5324.61977   1287.86379 -13255.99916  16173.23514 
##          218          219          220          221          222          223 
##   7546.98261   1244.38032  26400.86719  12170.86370   6954.42142  13637.29730 
##          224          225          226          227          228          229 
##  -4327.53214  -2130.98067   3403.69274    -12.28415   2384.40244   8647.11011 
##          230          231          232          233          234          235 
##   5462.59854  -2274.18365  -2178.68212   9089.54148 -11859.66639  -7597.50640 
##          236          237          238          239          240          241 
##  -8832.81102 -10370.23175   2833.07817   1097.30786  -8556.75570  -9229.85807 
##          242          243          244          245          246          247 
##   8873.43254  -8014.73533   2254.84281 -10544.22267  -4274.49099   1206.44792 
##          248          249          250          251          252          253 
##    776.63052 -12550.46357   3435.32215   1837.29353   3975.05559   1881.90141 
##          254          255          256          257          258          259 
##  -1422.55265  10877.56973  20586.03896   2843.12328  -4628.99767   3769.62216 
##          260          261          262          263          264          265 
##  -2041.23238   3400.74864  -5193.91620 -11217.04279  -5018.39764   -798.32064 
##          266          267          268          269          270          271 
##  -5464.85017   8514.27352  -4573.25519   3907.76310  -2403.02885   4140.05795 
##          272          273          274          275          276          277 
##    403.64787   6995.34136  -1741.92628  11701.29294  -4944.89932   1382.54703 
##          278          279          280          281          282          283 
##   -717.75055   7510.38881  -5420.50757  -3072.23282 -11589.12655  -2956.40720 
##          284          285          286          287          288          289 
##  18376.11017   7440.79489   2368.66123   -997.73420    544.98108   6039.39042 
##          290          291          292          293          294          295 
##   6506.78675 -19164.77081 -11454.77869  -8393.28418   9422.47459   2792.00887 
##          296          297          298          299          300          301 
##  -1470.41186  27115.50705   9677.95061   4485.25898   9096.30097   2412.72106 
##          302          303          304          305          306          307 
##  -1471.34269   7476.65211 -24730.92801  -3860.85750   -481.26405  -7268.98571 
##          308          309          310          311          312          313 
##  -4241.47954   2679.37203  -9456.71492  -3457.25162  -8402.42491   1378.93022 
##          314          315          316          317          318          319 
##  -3350.14176   1856.11330  -4290.11614  27247.81540  -1060.12932   2961.48971 
##          320          321          322          323          324          325 
##  10490.44815   5209.56339  31987.28388   4598.70192 -21443.79262   1397.09490 
##          326          327          328          329          330          331 
##    721.16587  -6846.09019  -2076.36837 -33594.11462    737.53402  -2454.09702 
##          332          333          334          335          336          337 
##   -238.76968  -3317.81391   3944.35575   -602.65532  -7120.65115  -3258.20619 
##          338          339          340          341          342          343 
##  -2326.08643  -7811.57233   3745.92183  -1505.39367  -1874.24322  -1130.62731 
##          344          345          346          347          348          349 
##     36.27839    332.56125  -1777.29213  -9605.03619 -13333.26657   2235.70788 
##          350          351          352          353          354          355 
##  -4423.31448  -3751.37032  -6069.18902   1675.19647   1284.82797   2632.87255 
##          356          357          358          359          360          361 
##  -3912.66101   -655.38248    529.84986   6852.89787     73.37948   -247.43492 
##          362          363          364          365          366          367 
##   2370.21653  -2978.65805  -1092.98111  -8955.86162  -4795.57011  -6363.25029 
##          368          369          370          371          372          373 
##  -5075.54131  -7362.70636   4930.12904    245.85837   6981.39583  -7820.68604 
##          374          375          376          377          378          379 
##  -2413.28994  -3533.79627  -2603.07268 -12589.21282   1824.84680 -10736.47868 
##          380          381          382          383          384          385 
##   5634.70405   9228.00031   2956.10229  -2592.95502   1417.08294   6542.03383 
##          386          387          388          389          390          391 
##  11168.16725  -6107.46669  -5637.92297   -405.77087   8313.92080   1518.88941 
##          392          393          394          395          396          397 
##  10918.04777 -10239.20669   2470.40194    396.84231    246.74723   -968.23482 
##          398          399          400          401          402          403 
##   -869.85267 -14787.25252   8308.94453  -1438.01622  -1619.88336   6743.93874 
##          404          405          406          407          408          409 
##  -8207.19858  -1523.44646  -2748.80326  -6020.72833  -3027.95291  -4072.76716 
##          410          411          412          413          414          415 
##  -8893.64606   6036.51546   1498.06252  -7533.09925  -7817.67143  14128.57322 
##          416          417          418          419          420          421 
##   3628.74319   4274.43812  -8283.80400  -4946.95759  -2778.65131   2653.72550 
##          422          423          424          425          426          427 
## -14197.46337  -2903.48007  -9204.57427   2947.23240   6879.52646   6425.30680 
##          428          429          430          431          432          433 
##  -4183.44776  -4298.15424  -4880.99092  -1928.03288  -5848.14090  -6739.63276 
##          434          435          436          437          438          439 
##  -6036.73315  -1460.96063   -923.05885  -5060.42078   2509.66521   4737.77404 
##          440          441          442          443          444          445 
##  -5198.01660  -2280.45114   1455.85475  -3976.58198   2708.71953  -6730.05922 
##          446          447          448          449          450          451 
## -12233.59613  -4578.78414   9587.36379  -2156.87119   4631.65661  -6026.56846 
##          452          453          454          455          456          457 
##  -1253.67306    251.11085   2883.92736 -12433.58829   3263.87023  -6834.56927 
##          458          459          460          461          462          463 
##   6416.35674   2861.83768   2334.74092  -4035.07559   1921.65770   -193.10735 
##          464          465          466          467          468          469 
##   1604.95486   -720.73234   3152.67420  -2855.56444   5604.08943  -7173.23440 
##          470          471          472          473          474          475 
##  -3154.63435  -2379.33639  -4827.07444   2855.35234   7635.68201  -6222.64340 
##          476          477          478          479          480          481 
##   1311.45018  -6360.91979  -2995.01040   1872.38753 -13084.81658  -9848.48257 
##          482          483          484          485          486          487 
##  -1257.10148    -43.04107  -1039.41701  -1426.66697  -9674.46791  11042.08491 
##          488          489          490          491          492          493 
##   6111.25219   7256.96273  -5641.32615   5190.67878   9086.92291   5801.20513 
##          494          495          496          497          498          499 
## -13751.57170 -10766.71804  -3586.65731  -1237.66693   -655.80335  -7760.12129 
##          500          501          502          503          504          505 
##    510.50724   4175.90265   5368.03653    487.44483    -97.07986  -7417.86431 
##          506          507          508          509          510          511 
##    426.84803  -5198.24805   1704.47107  -1439.22675  -8298.39045   -706.70028 
##          512          513          514          515          516          517 
##  -2784.33841   -692.11712   1222.32410  -9619.56990  -7849.48190  24229.32823 
##          518          519          520          521          522          523 
##   9634.04546   5638.85992  -5600.97468   2566.64182  16777.18875  11151.18792 
##          524          525          526          527          528          529 
## -24516.20014  -5288.98451  -3933.04685   4392.23312   -560.64912 -11303.70959 
##          530          531          532          533          534          535 
##   4246.92039  13738.68649  -5219.56178   4160.09181   5320.80104  -2049.08431 
##          536          537          538          539          540          541 
##  -4784.50525  -7289.61768  -2277.54497   8155.53454    -83.15276  -8350.21785 
##          542          543          544          545          546          547 
##   1650.17168   -776.32472    191.64000 -11208.43332 -11185.05550   1965.91684 
##          548          549          550          551          552          553 
##   6907.15333  -1458.05416    698.24124  -7868.46884   8451.50469    743.14291 
##          554          555          556          557          558          559 
## -12115.03848   9048.43177   8489.60192   -115.84792   4639.04638  -3811.39600 
##          560          561          562          563          564          565 
##  13892.94068  21213.74071  -6851.65544 -10015.08592   6503.10236    -72.04241 
##          566          567          568          569          570          571 
##   3165.72055  -7673.70274 -17555.80833   6510.21593   6247.65724   1684.81738 
##          572          573          574          575          576          577 
##   2875.69886   1536.76647  -2401.13156  14497.71019  -9938.23051  -6476.25885 
##          578          579          580          581          582          583 
##   8515.19688   2620.29275  -6792.39014   7300.97584  -4042.26234  -2992.91971 
##          584          585          586          587          588          589 
##  15502.94755 -14774.88763   8232.83096   -164.95729  -6442.12093   -947.34354 
##          590          591          592          593          594          595 
##     64.87295 -10839.85591   1664.37603  -7289.42395   2955.17885   8732.83814 
##          596          597          598          599          600          601 
##  -7681.91008   5707.18555   2558.61007   6665.83351  -3412.43279   5947.58564 
##          602          603          604          605          606          607 
##  -8527.52131   2072.62349   1077.62703   2942.04586   1286.46957    185.57643 
##          608          609          610          611          612          613 
##  -6019.70906   7898.21221  -1398.07726  -2777.34785  -3639.53271  -8394.84573 
##          614          615          616          617          618          619 
##  11832.52536   4749.70700  -9522.09348  11468.07003   5836.29340  -5809.45691 
##          620          621          622          623          624          625 
##  26158.57145 -13143.44318  -7017.93294   2960.68912  -4365.47064 -10773.21933 
##          626          627          628          629          630          631 
##  11167.70255 -21818.90136  -2496.96838   8596.36088  11009.25474  -1732.43987 
##          632          633          634          635          636          637 
##  33114.22294  -6905.51373   5446.82962   5116.05466  -2561.45547  -5612.00881 
##          638          639          640          641          642          643 
##  -2169.84754 -12643.26319  -2392.35196  -2026.32573  -2653.15895  -2981.57894 
##          644          645          646          647          648          649 
##   1701.21006   4304.84034  16816.11601  18327.95166    582.45760   4499.30028 
##          650          651          652          653          654          655 
##  10315.05441  19822.67152    351.73267 -28429.49145  -1558.47875  -2491.73841 
##          656          657          658          659          660          661 
##   1686.97835  -3373.72128 -10782.59457   1552.82102   4106.54244  -1144.39210 
##          662          663          664          665          666          667 
##  12900.12744   1175.77524   1629.74191 -11875.77686   1247.60568   1051.28925 
##          668          669          670          671          672          673 
##  -5305.01020  -7526.25437   1979.88916  -3809.64568   2587.65600  -3478.78748 
##          674          675          676          677          678          679 
##  -9425.83723  -8364.62357  -3015.06636    134.29156   2796.79223    643.13935 
##          680          681          682          683          684          685 
##  -3905.97562  -1879.60391  -1389.52197  -8315.59036   4598.47590  -2318.53180 
##          686          687          688          689          690          691 
##  -1472.42880    512.01368  10770.07054   9722.77016  10463.15307  -9853.87260 
##          692          693          694          695          696          697 
##  -3699.91294  -3268.77627   5754.94506 -10521.16073  -8007.99353  -8682.49470 
##          698          699          700          701          702          703 
##  -6322.05714  -4774.78495   3051.76005  -4453.29488  -1943.50388   4174.10798 
##          704          705          706          707          708          709 
##  31036.77738   9371.45542  23287.48310   1492.83758   8151.59149  22750.85434 
##          710          711          712          713          714          715 
##   6369.40529 -18383.26922   4696.35528  -5566.77078   -204.89361    381.72334 
##          716          717          718          719          720          721 
## -17360.46766  -5324.28342   3283.70382  -3071.41987 -13031.39248   4248.21304 
##          722          723          724          725          726          727 
##  -5598.80741    707.10429  -3974.63090 -12483.14748   1349.92627  -1892.71531 
##          728          729          730          731          732          733 
##  -9804.58867  17254.28705   1724.64015  -2777.88153   5663.98101  -8692.92333 
##          734          735          736          737          738          739 
##   -770.64688   8090.55329 -15415.04046  -5947.71229   7378.44127  -4831.22515 
##          740          741          742          743          744          745 
##    120.40474   1784.22232  -2004.90812  -5215.38183   6372.35880  -6329.10427 
##          746          747          748          749          750          751 
##  22654.46475   7742.18401  -2041.79493  -7375.13637  23345.44757  -4398.92879 
##          752          753          754          755          756          757 
##   1302.83604 -14512.89868  56046.52202  26773.98639  14923.90030 -10820.49365 
##          758          759          760          761          762          763 
##  10468.20827   7173.89818   5672.02549 -46521.62839 -16221.33191    941.37890 
##          764          765          766          767          768          769 
##  -2545.89448  -3483.98382 122821.12870  19122.89337  43528.06468  22358.51605 
##          770          771          772          773          774          775 
##  11845.99422  15627.47217  25511.56034 -99034.15516  -6830.02714 -35890.89887 
##          776          777          778          779          780          781 
##   1727.39820  -1243.90345   3380.20128  -7437.22236  -1459.19165  -1959.93420 
##          782          783          784          785          786          787 
##   3410.02555  -7176.14308  -2249.78924   3907.01929   2245.71180  -2782.73052 
##          788          789          790          791          792          793 
##  -4068.07959   1722.47836   2833.33943    -76.01328  -6728.21115  -5799.06487 
##          794          795          796          797          798          799 
##  -1158.00849  -1274.63715  -7829.37206  -2358.25346  -3272.72536  -2669.14162 
##          800 
##  10720.82102 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17242.47  20095.23  24357.55  24074.97  26432.95  23760.37  24478.09  19700.86 
##        10        11        12        13        14        15        16        17 
##  19436.90  16774.45  17553.44  14276.42  14327.69  14993.64  16693.55  15010.27 
##        18        19        20        21        22        23        24        25 
##  16046.97  15419.27  22515.61  21597.82  21076.66  22970.37  22295.04  22948.87 
##        26        27        28        29        30        31        32        33 
##  24798.44  18714.92  20444.46  28297.15  28354.92  28027.20  25651.96  27057.14 
##        34        35        36        37        38        39        40        41 
##  30907.72  31256.82  32668.76  30174.49  34146.23  37360.60  34410.94  31215.57 
##        42        43        44        45        46        47        48        49 
##  30061.08  20622.15  28155.62  30596.87  31688.21  38539.72  38033.31  42704.64 
##        50        51        52        53        54        55        56        57 
##  46949.21  39632.81  34190.62  29203.57  22334.56  28636.89  25210.82  21501.17 
##        58        59        60        61        62        63        64        65 
##  25918.17  27180.19  27477.78  27890.41  23753.25  40377.04  42224.62  37461.26 
##        66        67        68        69        70        71        72        73 
##  41676.44  46601.73  57290.80  55300.68  40514.96  38015.93  41039.75  35328.72 
##        74        75        76        77        78        79        80        81 
##  30772.05  21457.28  24665.03  20582.76  22672.91  17558.45  19577.82  18802.83 
##        82        83        84        85        86        87        88        89 
##  17828.65  15893.80  17174.08  20789.20  25215.58  26207.38  26232.39  26850.33 
##        90        91        92        93        94        95        96        97 
##  30983.49  29811.76  30813.07  28873.71  28071.96  28440.83  28848.21  22413.08 
##        98        99       100       101       102       103       104       105 
##  25434.02  18450.85  17304.74  15341.63  15641.97  16320.69  20802.08  19883.79 
##       106       107       108       109       110       111       112       113 
##  23401.76  23191.32  24870.72  27753.12  25246.34  21682.85  21958.81  24610.26 
##       114       115       116       117       118       119       120       121 
##  35496.02  33685.33  35526.41  38530.59  40490.33  38174.23  32985.12  29319.35 
##       122       123       124       125       126       127       128       129 
##  31406.25  29682.84  30866.71  38481.33  38123.09  37182.80  34040.20  35824.88 
##       130       131       132       133       134       135       136       137 
##  41233.31  40672.51  31857.02  33124.29  36320.28  32723.91  31108.03  30191.25 
##       138       139       140       141       142       143       144       145 
##  26741.55  28158.74  27927.92  25609.70  27638.08  26260.15  19849.78  22886.23 
##       146       147       148       149       150       151       152       153 
##  20708.67  23691.60  24232.65  25826.14  26008.57  27665.96  28969.24  32007.31 
##       154       155       156       157       158       159       160       161 
##  27468.65  26730.17  24280.66  30186.25  41672.57  39999.55  37359.36  42389.42 
##       162       163       164       165       166       167       168       169 
##  43800.73  47223.30  42680.39  37978.42  43414.24  59748.19  61964.63  60375.64 
##       170       171       172       173       174       175       176       177 
##  57195.15  55591.28  58299.12  57321.31  49598.41  52427.42  56177.33  56213.59 
##       178       179       180       181       182       183       184       185 
##  63356.32  53840.11  50584.72  41375.48  32898.34  36413.44  46536.59  45991.54 
##       186       187       188       189       190       191       192       193 
##  51955.90  57712.06  68516.05  73809.19  67491.60  67685.41  74769.49  70437.52 
##       194       195       196       197       198       199       200       201 
##  65951.62  55174.52  49193.04  50620.11  46206.23  38359.79  44797.04  43020.99 
##       202       203       204       205       206       207       208       209 
##  42658.25  43137.53  49944.77  58851.29  58480.17  60208.34  61867.35  65665.91 
##       210       211       212       213       214       215       216       217 
##  75150.86  67218.27  55384.34  49982.66  40961.48  37913.28  41033.00  31033.76 
##       218       219       220       221       222       223       224       225 
##  48040.30  55375.33  56278.99  79088.71  86598.29  88605.42  96211.53  87144.84 
##       226       227       228       229       230       231       232       233 
##  81131.59  80712.71  77356.17  76516.03  81262.26  82629.18  77053.82  72257.46 
##       234       235       236       237       238       239       240       241 
##  77922.09  64543.93  56564.95  48499.95  40095.21  44295.26  46452.18  39890.14 
##       242       243       244       245       246       247       248       249 
##  33557.42  43859.88  38095.59  42038.94  34287.78  32991.12  36653.51  39482.89 
##       250       251       252       253       254       255       256       257 
##  30294.53  36244.14  40052.94  45257.81  47981.41  47473.00  57793.96  75325.16 
##       258       259       260       261       262       263       264       265 
##  75139.85  68437.52  69922.23  66135.68  67584.63  61330.19  50583.97  46603.61 
##       266       267       268       269       270       271       272       273 
##  46813.42  42912.58  51733.83  47999.67  52154.46  50267.37  54342.64  54639.23 
##       274       275       276       277       278       279       280       281 
##  60668.35  58297.99  67989.76  61902.74  62113.18  60459.04  66213.08  59931.38 
##       282       283       284       285       286       287       288       289 
##  56488.56  46020.55  44414.18  61679.92  67220.77  67631.02  65043.59  64129.18 
##       290       291       292       293       294       295       296       297 
##  68137.93  72055.77  53015.35  43098.14  37097.53  47438.99  50687.13  49799.35 
##       298       299       300       301       302       303       304       305 
##  74042.76  79999.74  80668.70  85290.14  83485.20  78505.78  81979.36  56829.29 
##       306       307       308       309       310       311       312       313 
##  53083.12  52762.27  46540.34  43744.34  47354.71  39892.39  38612.00  33162.93 
##       314       315       316       317       318       319       320       321 
##  36954.86  36134.60  39973.54  37954.04  63790.70  61627.65  63254.41  71268.15 
##       322       323       324       325       326       327       328       329 
##  73660.14  99191.58  97566.08  73349.05  72144.55  70498.66  62434.65  59551.26 
##       330       331       332       333       334       335       336       337 
##  29440.89  33135.67  33576.06  35900.53  35240.07  41018.37  42096.08  37334.35 
##       338       339       340       341       342       343       344       345 
##  36547.23  36674.14  31983.94  37994.68  38659.39  38918.34  39795.86  41585.30 
##       346       347       348       349       350       351       352       353 
##  43410.86  43162.04  36092.84  26642.15  31997.31  30856.08  30445.33  28057.09 
##       354       355       356       357       358       359       360       361 
##  32745.17  36506.84  40979.23  39164.67  40427.44  42570.10  49979.91  50531.58 
##       362       363       364       365       366       367       368       369 
##  50733.64  53201.66  50680.12  50123.58  42754.28  39945.54  36114.97  33889.28 
##       370       371       372       373       374       375       376       377 
##  29939.30  37241.57  39533.03  47434.11  41393.86  40839.94  39374.36  38906.21 
##       378       379       380       381       382       383       384       385 
##  29755.87  34363.05  27401.01  35636.57  45990.04  49562.53  47832.49  49828.11 
##       386       387       388       389       390       391       392       393 
##  56060.55  65564.75  58762.64  53219.91  52948.08  60342.25  60866.67  69552.49 
##       394       395       396       397       398       399       400       401 
##  58636.60  60206.59  59765.82  59248.66  57732.57  56491.68  43224.06  51826.73 
##       402       403       404       405       406       407       408       409 
##  50825.17  49789.35  56203.34  48731.02  48040.80  46364.16  42032.81  40861.20 
##       410       411       412       413       414       415       416       417 
##  38921.22  33003.63  40892.08  43824.24  38485.96  33564.43  48465.69  52318.13 
##       418       419       420       421       422       423       424       425 
##  56255.23  48709.39  45025.37  43698.70  47292.32  35688.34  35417.00  29664.34 
##       426       427       428       429       430       431       432       433 
##  35265.33  43609.55  50515.45  47274.44  44337.28  41256.32  41144.28  37615.06 
##       434       435       436       437       438       439       440       441 
##  33745.73  30974.25  32553.49  34406.56  32407.19  37283.08  43501.02  40246.88 
##       442       443       444       445       446       447       448       449 
##  39952.29  42964.72  40846.57  44844.06  40081.45  31095.78  29930.92  41310.59 
##       450       451       452       453       454       455       456       457 
##  40991.49  46654.00  42281.39  42631.75  44255.50  47981.16  37835.13  42694.14 
##       458       459       460       461       462       463       464       465 
##  38108.21  45692.45  49219.54  51845.36  48568.34  50913.82  51115.76  52866.30 
##       466       467       468       469       470       471       472       473 
##  52362.90  55312.56  52635.48  57696.81  50943.21  48549.34  47132.65  43750.22 
##       474       475       476       477       478       479       480       481 
##  47513.89  54992.21  49407.98  51114.63  45893.01  44268.76  47107.39  36500.34 
##       482       483       484       485       486       487       488       489 
##  30048.96  31922.04  34624.13  36117.10  37084.90  30712.92  43268.32  49941.89 
##       490       491       492       493       494       495       496       497 
##  56785.90  51486.75  56329.51  63978.51  67797.57  54026.29  44585.23  42606.24 
##       498       499       500       501       502       503       504       505 
##  42930.09  43722.84  38198.49  40602.24  45914.39  51607.41  52318.51  52429.29 
##       506       507       508       509       510       511       512       513 
##  46118.58  47461.25  43712.96  46473.94  46138.96  39842.13  40975.48  40148.97 
##       514       515       516       517       518       519       520       521 
##  41256.82  43902.14  36727.91  31997.81  55935.38  64112.43  67772.69  61138.50 
##       522       523       524       525       526       527       528       529 
##  62480.67  76093.53  83084.20  57984.27  52844.05  49531.77  53919.51  53424.85 
##       530       531       532       533       534       535       536       537 
##  43588.79  48590.60  61276.42  55786.34  59190.77  63186.51  60233.22  55254.05 
##       538       539       540       541       542       543       544       545 
##  48703.26  47356.47  55309.44  55059.36  47604.54  49832.61  49658.93  50354.15 
##       546       547       548       549       550       551       552       553 
##  40984.48  32803.94  37154.42  45287.20  45083.76  46793.04  40790.92  49821.86 
##       554       555       556       557       558       559       560       561 
##  50979.47  40738.28  50298.26  58176.71  57540.38  61145.25  56904.06  68687.97 
##       562       563       564       565       566       567       568       569 
##  85409.80  75481.09  64021.90  68449.90  66570.57  67759.56  59312.81  43270.07 
##       570       571       572       573       574       575       576       577 
##  50292.63  56209.47  57394.59  59474.23  60122.56  57243.29  69514.23  58866.54 
##       578       579       580       581       582       583       584       585 
##  52577.09  60193.71  61700.68  54781.02  61059.98  56627.35  53666.05  67263.03 
##       586       587       588       589       590       591       592       593 
##  52662.74  60021.53  59112.12  52821.91  52125.70  52402.28  43099.77  45902.14 
##       594       595       596       597       598       599       600       601 
##  40517.96  44772.16  53552.77  46870.81  52741.39  55123.88  60804.15  56954.70 
##       602       603       604       605       606       607       608       609 
##  61777.95  53329.95  55213.66  55991.53  58304.24  58879.42  58419.28  52585.22 
##       610       611       612       613       614       615       616       617 
##  59660.79  57717.06  54808.53  51508.13  44457.19  55990.15  59885.24  50802.79 
##       618       619       620       621       622       623       624       625 
##  61225.28  65418.46  58895.43  81166.73  66260.22  58574.45  60581.33  55925.51 
##       626       627       628       629       630       631       632       633 
##  46241.87  56970.33  37488.40  37348.35  46935.46  57438.73  55479.49  84264.94 
##       634       635       636       637       638       639       640       641 
##  74431.88  76636.95  78277.46  72993.44  65698.42  62326.12  50207.35  48572.47 
##       642       643       644       645       646       647       648       649 
##  47461.87  45941.15  44322.65  47004.73  51631.17  66631.33  81083.83  78201.56 
##       650       651       652       653       654       655       656       657 
##  79107.09  84990.04  98460.98  93209.35  63421.34  60868.17  57816.59  58803.15 
##       658       659       660       661       662       663       664       665 
##  55237.17  45631.18  48020.17  52346.39  51537.02  63121.37  62998.83  63288.92 
##       666       667       668       669       670       671       672       673 
##  51721.82  53084.00  54104.44  49434.11  43402.11  46442.93  44037.06  47530.64 
##       674       675       676       677       678       679       680       681 
##  45278.69  38102.34  32749.92  32747.42  35501.78  40243.00  42507.83  40508.46 
##       682       683       684       685       686       687       688       689 
##  40532.09  40981.73  35313.10  41654.82  41151.29  41451.13  43450.50  54179.09 
##       690       691       692       693       694       695       696       697 
##  62652.85  70717.73  59993.77  55993.78  52870.05  58034.16  48308.14  41994.92 
##       698       699       700       701       702       703       704       705 
##  35878.77  32591.50  31068.53  36585.87  34846.08  35520.03  41464.51  70179.69 
##       706       707       708       709       710       711       712       713 
##  76350.23  93931.45  90243.55  92843.86 107898.17 106736.55  84054.50  84402.49 
##       714       715       716       717       718       719       720       721 
##  75724.04  72821.13  70793.75  53490.00  48879.44  52378.28  49878.25  38972.36 
##       722       723       724       725       726       727       728       729 
##  44551.09  40815.18  43064.63  40935.72  31625.07  35583.43  36209.87  29833.14 
##       730       731       732       733       734       735       736       737 
##  47935.65  50187.60  48217.73  53882.49  46274.50  46549.59  54546.33  40971.86 
##       738       739       740       741       742       743       744       745 
##  37376.99  45894.51  42662.88  44168.35  46942.34  46053.81  42466.07  49468.25 
##       746       747       748       749       750       751       752       753 
##  44479.82  65482.10  70812.51  66914.42  58834.41  78651.07  71712.16  70629.33 
##       754       755       756       757       758       759       760       761 
##  55838.48 104651.16 121754.10 126351.78 107842.65 110275.53 109521.55 107547.06 
##       762       763       764       765       766       767       768       769 
##  60135.19  45157.91  47070.75  45692.70  43665.44 152442.39 156887.65 182139.63 
##       770       771       772       773       774       775       776       777 
## 185712.86 179639.10 177632.73 184527.87  81551.60  72123.04  38434.32  41873.76 
##       778       779       780       781       782       783       784       785 
##  42283.51  46689.51  41077.76  41398.36  41240.69  45802.86  40530.22  40227.12 
##       786       787       788       789       790       791       792       793 
##  45350.72  48381.16  46632.37  43976.66  46720.52  50094.44  50501.07  45034.49 
##       794       795       796       797       798       799       800 
##  41063.01  41649.07  42059.94  36682.40  36764.30  36035.57  35926.04 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8126
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    4.878707   0.7459174    3.757248
## t2* 2601.669146 167.0204055  877.681266
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.071748       4.841002   12.85602
## 2    lag_depvar 1578.807680    2641.123080 4415.57612

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Dec 30 00:51:50 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Dec 30 00:51:58 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Dec 30 00:52:06 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Dec 30 00:52:14 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Dec 30 00:52:22 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Dec 30 00:52:30 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Dec 30 00:52:38 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Dec 30 00:52:46 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Dec 30 00:52:54 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Dec 30 00:53:02 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 6.190545 5.195333 5.410333 5.849167 6.677250
Comida 329.464273 366.009167 312.386750 317.896583 342.723233
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000
Electricidad 88.688727 38.104750 47.072333 29.523000 43.777483
Enceres 26.169818 18.259750 24.219750 14.801167 24.056333
Farmacia 0.000000 10.704083 2.835000 13.996083 8.314383
Gas/Bencina 48.319273 42.636000 45.575000 13.583667 32.669133
Diosi 34.869000 55.804250 31.180667 52.687833 42.022733
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000
VTR 17.992727 12.829167 25.156667 19.086917 18.902633
Netflix 1.517909 8.713833 7.151583 7.028750 6.506567
Otros 74.454545 5.481667 5.000000 0.000000 16.537733
Total 627.666818 563.738000 505.988083 474.453167 542.187483
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2557, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-12-31 00:04:58 sería de: 38.604 pesos// Percentil 95% más alto proyectado: 41.515,44

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 38416.82 38416.67
Lo.80 38418.31 38418.00
Point.Forecast 38603.86 39152.75
Hi.80 40264.77 44003.75
Hi.95 41172.74 46571.71


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,2) 
## 
## Coefficients:
##           ma1      ma2
##       -0.5610  -0.3022
## s.e.   0.1113   0.1114
## 
## sigma^2 = 38132:  log likelihood = -461.44
## AIC=928.88   AICc=929.25   BIC=935.58
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1     xreg
##       0.3406  32.7856
## s.e.  0.1029   0.8890
## 
## sigma^2 = 33275:  log likelihood = -462.81
## AIC=931.63   AICc=931.99   BIC=938.37
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 969.7588 743.6487 776.0567
Lo.80 1100.4934 888.3288 881.5743
Point.Forecast 1347.4570 1161.6360 1121.5821
Hi.80 1594.4205 1455.6163 1446.9683
Hi.95 1725.1551 1611.2400 1655.8181


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))